Removal of Color Pigments From Corn Distillers Dried Grains With Solubles (DDGS) to Produce an Upgraded Food Ingredient
Bibliographic record
Abstract
Processing steps including bleaching, deodorizing, and milling are imperative for improving the functionality of distillers grains in various food matrices, as well as improving consumer acceptance. Utilization of distillers grains in food products is of particular interest. Various parameters were explored for the removal of pigments, including raw DDGS (diameter 0.384) or milled DDGS (0.329 mm), number of extractions (1, 2, or 3), time (30, 60, or 90 min.), and ethanol concentration (5, 10, or 15 mL/g). Altogether, the experimental design was a 2 x 3 x 3 x 3 factorial, resulting in 54 trials, which were each replicated twice. Physical and chemical properties of the resulting DDGS were analyzed. Protein content was impacted by time and number of extractions. A decrease in lipid content resulted in an inverse increase in protein content. Lipid and pigment analysis showed similar decreasing trends, signifying that lipid contents decreased while increasing solvent extraction time, ethanol concentration, and number of extractions. Physical property analysis showed ethanol extraction to be a moderately effective bleaching technique for DDGS. Chemical property data showed that the treatments were extremely effective in reducing lipid and pigment values, while increasing protein. Effective removal of pigments can improve the color of food products containing DDGS, which can lead to greater consumer acceptability of this ingredient.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".